Training and Inference Details.
Our method is implemented in PyTorch [1]}. The network is trained on a NVIDIA Tesla V100 (32G) GPU. The Adam [2]} optimizer is used with an initial learning rate of 0.001 and is modified using the one-cycle learning rate policy [3]}. We train the model for 80 epochs on the KITTI dataset [4]} and 10 epochs on the Waymo Open Dataset [5]}. We use a batch size of 4 for KITTI [4]} and a batch size of 2 for Waymo. The values \(\lambda _{\mathrm {depth}} = 3.0, \lambda _{\mathrm {cls}} = 1.0, \lambda _{\mathrm {reg}} = 2.0, \lambda _{\mathrm {dir}} = 0.2\)  are used for the loss weighting factors in Equation REF . We employ horizontal flip as our data augmentation and train one model for all classes. During inference, we filter boxes with a score threshold of 0.1 and apply non-maximum suppression (NMS) with an IoU threshold of 0.01.
